{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\XUE XINDONG\Desktop\educ_health\World Development Submissions\part4 educ_health_20191007.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Oct 2019, 15:00:31

{com}. do "C:\Users\XUEXIN~1\AppData\Local\Temp\STD3f28_000000.tmp"
{txt}
{com}. use "C:\Users\XUE XINDONG\Desktop\educ_health\meta_educ_health(20190928).dta",
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\XUEXIN~1\AppData\Local\Temp\STD3f28_000000.tmp"
{txt}
{com}. 
. ****************************************************************************
. **************TABLE 11: Robustness Checks on FULL SAMPLE******************** 
. ****************************************************************************
. 
. ******************************************************************************
. *DEFINITION OF BASIC VARIABLES
. ******************************************************************************
. gen tstat=tstatistics
{txt}
{com}. gen negative = 1
{txt}
{com}. replace negative = -1 if BadHealth == 1 & BadEduc == 0
{txt}(3,330 real changes made)

{com}. replace negative = -1 if BadHealth == 0 & BadEduc == 1
{txt}(80 real changes made)

{com}. replace tstat = tstat*negative
{txt}(3,397 real changes made)

{com}. gen df = n-k1
{txt}
{com}. gen r = tstat/sqrt(tstat^2+df)
{txt}
{com}. gen varR = (1-r^2)/df
{txt}
{com}. gen seR = sqrt(varR)
{txt}
{com}. gen pcc = r
{txt}
{com}. gen abststat = abs(tstat)
{txt}
{com}. gen abspcc = abs(pcc)
{txt}
{com}. 
. ******************************************************************************
. * Distribution of t-stats and PCCs before kicking out outliers
. ******************************************************************************
. summ tstat, detail

                            {txt}tstat
{hline 61}
      Percentiles      Smallest
 1%    {res}-3.366447            -20
{txt} 5%    {res}-1.705179         -19.75
{txt}10%    {res}-.6745101      -12.80742       {txt}Obs         {res}      4,718
{txt}25%    {res} .5986928            -12       {txt}Sum of Wgt. {res}      4,718

{txt}50%    {res} 1.959998                      {txt}Mean          {res} 3.053543
                        {txt}Largest       Std. Dev.     {res} 6.912549
{txt}75%    {res} 3.290574            104
{txt}90%    {res}        7            109       {txt}Variance      {res} 47.78333
{txt}95%    {res} 11.94444            125       {txt}Skewness      {res} 7.950496
{txt}99%    {res}     27.7       137.5762       {txt}Kurtosis      {res} 105.9487
{txt}
{com}. summ pcc, detail

                             {txt}pcc
{hline 61}
      Percentiles      Smallest
 1%    {res}-.0611414      -.1993045
{txt} 5%    {res} -.018811      -.1753322
{txt}10%    {res}-.0071823      -.1448239       {txt}Obs         {res}      4,718
{txt}25%    {res}  .003816      -.1299282       {txt}Sum of Wgt. {res}      4,718

{txt}50%    {res} .0150329                      {txt}Mean          {res} .0311541
                        {txt}Largest       Std. Dev.     {res} .0625404
{txt}75%    {res} .0438438        .854579
{txt}90%    {res} .0878176       .9144696       {txt}Variance      {res} .0039113
{txt}95%    {res} .1214882       .9151465       {txt}Skewness      {res} 5.387409
{txt}99%    {res} .2419359       .9489114       {txt}Kurtosis      {res} 57.61067
{txt}
{com}. summ df, detail 

                             {txt}df
{hline 61}
      Percentiles      Smallest
 1%    {res}      250             12
{txt} 5%    {res}      612             47
{txt}10%    {res}      985             47       {txt}Obs         {res}      4,718
{txt}25%    {res}     3372             48       {txt}Sum of Wgt. {res}      4,718

{txt}50%    {res}     9349                      {txt}Mean          {res} 67163.41
                        {txt}Largest       Std. Dev.     {res} 290314.3
{txt}75%    {res}    26442        3781410
{txt}90%    {res}   106691        3781410       {txt}Variance      {res} 8.43e+10
{txt}95%    {res}   205749        3781410       {txt}Skewness      {res} 8.915053
{txt}99%    {res}  1823897        3781410       {txt}Kurtosis      {res} 92.25565
{txt}
{com}. 
. 
. ******************************************************************************
. * FAT/PET
. ******************************************************************************
. ******************************************************************************
. * Calculating study weights based on number of estimates per study
. ******************************************************************************
. bysort ID: egen numberests = count(ID)
{txt}
{com}. gen weight = 1/numberests
{txt}
{com}. 
. 
. ******************************************************************************
. * FIXED EFFECTS
. ******************************************************************************
. 
. gen feprecisionR = 1/seR
{txt}
{com}. gen fetstatR = r/seR
{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 1)
. ******************************************************************************
. regress fetstatR feprecisionR,  vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(1, 106)         =  {res}     3.11
                                                {txt}Prob > F          = {res}    0.0807
                                                {txt}R-squared         = {res}    0.0508
                                                {txt}Root MSE          =    {res} 6.7355

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0075741{col 26}{space 2}  .004295{col 37}{space 1}    1.76{col 46}{space 3}0.081{col 54}{space 4}-.0009412{col 67}{space 3} .0160894
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 1.857237{col 26}{space 2} .5935648{col 37}{space 1}    3.13{col 46}{space 3}0.002{col 54}{space 4} .6804375{col 67}{space 3} 3.034037
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column (2)
. ******************************************************************************
. regress fetstatR feprecisionR  [pweight = weight],  vce(cluster ID)
{txt}(sum of wgt is 107)

Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(1, 106)         =  {res}     2.41
                                                {txt}Prob > F          = {res}    0.1238
                                                {txt}R-squared         = {res}    0.0808
                                                {txt}Root MSE          =    {res} 11.543

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0104709{col 26}{space 2} .0067488{col 37}{space 1}    1.55{col 46}{space 3}0.124{col 54}{space 4}-.0029092{col 67}{space 3} .0238509
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} 2.361991{col 26}{space 2} .8366786{col 37}{space 1}    2.82{col 46}{space 3}0.006{col 54}{space 4} .7031944{col 67}{space 3} 4.020788
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * RANDOM EFFECTS 
. ******************************************************************************
. //metan r seR, random
. metareg r seR , wsse(seR)

{txt}Meta-regression{col 55}Number of obs{col 70}= {res}   4718
REML{txt} estimate of between-study variance{col 55}tau2{col 70}={res} .002688
{txt}% residual variation due to heterogeneity{col 55}I-squared_res{col 70}= {res} 97.80%
{txt}Proportion of between-study variance explained{col 55}Adj R-squared {col 70}= {res}  6.28%
With{txt} Knapp-Hartung modification
{res}{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}           r{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}seR {c |}{col 14}{res}{space 2} 1.325754{col 26}{space 2} .0776744{col 37}{space 1}   17.07{col 46}{space 3}0.000{col 54}{space 4} 1.173476{col 67}{space 3} 1.478032
{txt}{space 7}_cons {c |}{col 14}{res}{space 2} .0125936{col 26}{space 2} .0012616{col 37}{space 1}    9.98{col 46}{space 3}0.000{col 54}{space 4} .0101203{col 67}{space 3} .0150669
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. scalar tau2 =  e(tau2)
{txt}
{com}. gen revarR = varR + tau2
{txt}
{com}. gen reseR = sqrt(revarR)
{txt}
{com}. 
. 
. ******************************************************************************
. * Correcting for heteroskedasticity
. ******************************************************************************
. gen reprecisionR = 1/reseR
{txt}
{com}. gen retstatR = r/reseR
{txt}
{com}. gen repubbiasR = seR/reseR
{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 3)
. ******************************************************************************
. regress retstatR  reprecisionR repubbiasR,  noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(2, 106)         =  {res}    50.64
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2466
                                                {txt}Root MSE          =    {res} 1.0353

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0125936{col 26}{space 2} .0039617{col 37}{space 1}    3.18{col 46}{space 3}0.002{col 54}{space 4} .0047391{col 67}{space 3} .0204481
{txt}{space 2}repubbiasR {c |}{col 14}{res}{space 2} 1.325754{col 26}{space 2}  .329631{col 37}{space 1}    4.02{col 46}{space 3}0.000{col 54}{space 4} .6722281{col 67}{space 3} 1.979279
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column 4)
. ******************************************************************************
. regress retstatR  reprecisionR   repubbiasR [pweight = weight] , noc vce(cluster ID)
{txt}(sum of wgt is 107)

Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(2, 106)         =  {res}    59.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3404
                                                {txt}Root MSE          =    {res} 1.2935

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0049402{col 26}{space 2} .0072078{col 37}{space 1}    0.69{col 46}{space 3}0.495{col 54}{space 4}-.0093501{col 67}{space 3} .0192304
{txt}{space 2}repubbiasR {c |}{col 14}{res}{space 2} 2.644554{col 26}{space 2} .7329587{col 37}{space 1}    3.61{col 46}{space 3}0.000{col 54}{space 4} 1.191393{col 67}{space 3} 4.097716
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. 
. ******************************************************************************
. * *PEESE
. ******************************************************************************
. 
. ******************************************************************************
. * FIXED EFFECTS
. ******************************************************************************
. 
. ******************************************************************************
. * Equal weight to each estimate (Column 1)
. ******************************************************************************
. regress fetstatR feprecisionR seR,  noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(2, 106)         =  {res}    31.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1853
                                                {txt}Root MSE          =    {res} 6.8217

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2} .0111628{col 26}{space 2} .0034538{col 37}{space 1}    3.23{col 46}{space 3}0.002{col 54}{space 4} .0043153{col 67}{space 3} .0180102
{txt}{space 9}seR {c |}{col 14}{res}{space 2} 52.11479{col 26}{space 2} 13.68763{col 37}{space 1}    3.81{col 46}{space 3}0.000{col 54}{space 4} 24.97774{col 67}{space 3} 79.25184
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column (2)
. ******************************************************************************
. regress fetstatR feprecisionR seR [pweight = weight], noc vce(cluster ID)
{txt}(sum of wgt is 107)

Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(2, 106)         =  {res}    28.96
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.1857
                                                {txt}Root MSE          =    {res} 11.621

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    fetstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
feprecisionR {c |}{col 14}{res}{space 2}  .013356{col 26}{space 2} .0058778{col 37}{space 1}    2.27{col 46}{space 3}0.025{col 54}{space 4} .0017026{col 67}{space 3} .0250093
{txt}{space 9}seR {c |}{col 14}{res}{space 2} 61.46744{col 26}{space 2} 14.59079{col 37}{space 1}    4.21{col 46}{space 3}0.000{col 54}{space 4} 32.53978{col 67}{space 3}  90.3951
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * RANDOM EFFECTS 
. ******************************************************************************
. ge  repubbiasRR = varR/reseR
{txt}
{com}. 
. ******************************************************************************
. * Equal weight to each estimate (Column 3)
. ******************************************************************************
. regress retstatR  reprecisionR   repubbiasRR,  noc vce(cluster ID)

{txt}Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(2, 106)         =  {res}    48.03
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2279
                                                {txt}Root MSE          =    {res}  1.048

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0241741{col 26}{space 2} .0031717{col 37}{space 1}    7.62{col 46}{space 3}0.000{col 54}{space 4} .0178859{col 67}{space 3} .0304623
{txt}{space 1}repubbiasRR {c |}{col 14}{res}{space 2} 18.19581{col 26}{space 2} 7.180674{col 37}{space 1}    2.53{col 46}{space 3}0.013{col 54}{space 4} 3.959421{col 67}{space 3} 32.43219
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. ******************************************************************************
. * Equal weight to each study (Column 4)
. ******************************************************************************
. regress retstatR  reprecisionR repubbiasRR  [pweight = weight] , noc vce(cluster ID)
{txt}(sum of wgt is 107)

Linear regression                               Number of obs     = {res}     4,718
                                                {txt}F(2, 106)         =  {res}    55.20
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3457
                                                {txt}Root MSE          =    {res} 1.2883

{txt}{ralign 78:(Std. Err. adjusted for {res:107} clusters in ID)}
{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}    retstatR{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      t{col 46}   P>|t|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
reprecisionR {c |}{col 14}{res}{space 2} .0263038{col 26}{space 2} .0030787{col 37}{space 1}    8.54{col 46}{space 3}0.000{col 54}{space 4}    .0202{col 67}{space 3} .0324076
{txt}{space 1}repubbiasRR {c |}{col 14}{res}{space 2} 38.80006{col 26}{space 2} 9.615377{col 37}{space 1}    4.04{col 46}{space 3}0.000{col 54}{space 4} 19.73664{col 67}{space 3} 57.86348
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. 
. //log close
.  
. 
{txt}end of do-file

{com}. do "C:\Users\XUEXIN~1\AppData\Local\Temp\STD3f28_000000.tmp"
{txt}
{com}. clear
{txt}
{com}. set more off
{txt}
{com}. set type double
{txt}
{com}. graph drop _all
{txt}
{com}. program drop _all
{txt}
{com}. set scheme s2mono
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\XUE XINDONG\Desktop\educ_health\World Development Submissions\part4 educ_health_20191007.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 7 Oct 2019, 15:02:20
{txt}{.-}
{smcl}
{txt}{sf}{ul off}